A data mining approach to reduce the false alarm rate of patient monitors

Abstract

Abstract-Patient monitors in intensive care units trigger alarms if the state of the patient deteriorates or if there is a technical problem, e.g. loose sensors. Monitoring systems have a high sensitivity in order to detect relevant changes in the patient state. However, multiple studies revealed a high rate of either false or clinically not relevant alarms. It was found that the high rate of false alarms has a negative impact on both patients and staff. In this study we apply data mining methods to reduce the false alarm rate of monitoring systems. We follow a multi-parameter approach where multiple signals of a monitoring system are used to classify given alarm situations. In particular we focus on five alarm types and let our system decide whether the triggered alarm is clinically relevant or can be considered as a false alarm. Several classification algorithms (Naive Bayes, Decision Trees, SVM, kNN and Multi-Layer Perceptron) were evaluated. For training and test sets a subset of the freely available MIMIC II database was used. Alarm-specific classification accuracy was between 78.56% and 98.84%. Suppression rates for false alarms were between 75.24% and 99.23%. Classification results strongly depend on available training data, which is still limited in the intensive care domain. However, this study shows that data mining methods are useful and applicable for alarm classification.

Pattern discovery in critical alarms originating from neonates under intensive care

“…Some of the organizational and institutional reforms that have helped reduce alarm fatigue include customization of alarm thresholds to patient characteristics, daily electrode change for ECG, employing disposable ECG electrodes, judging the appropriateness of continuous physiological monitoring for different patient populations, selecting devices with low falsealarm frequencies and rapid communication of alarms to healthcare workers by employing pagers, wireless phones etc (Ahlborn et al 2000, Atzema et al 2006, Graham and Cvach 2010, Bonzheim et al 2011, Cvach et al 2013, Albert et al 2015, van Pul et al 2015. From a technical point of view, both improved signal processing and more sophisticated classification techniques (using both single and multiple sources of physiological signals) have shown promise in reducing nuisance alarms (Schoenberg et al 1999, Imhoff et al 2006, Imhoff et al 2009, Borowski et al 2011, Baumgartner et al 2012. Approaching this problem from a different perspective, a study by Görges et al that used approximately 1200 alarms from an intensive care unit, showed that delaying the onset of alarms can eliminate many ineffective ones.…”

Computer Assisted Patient Monitoring: Associated Patient, Clinical and ECG Characteristics and Strategy to Minimize False Alarms

“…The ultimate goal of this effort is to create a database that can be used to develop and test new algorithms that incorporate not only ECG, but existing physiologic signals (i.e., SpO2 or arterial blood pressure). It should be noted that a number of studies have been published using varied algorithm-based approaches, mostly machine learning, to address false lethal arrhythmia alarms [39][40][41][42][43][44][45][46][47][48][49]. While several of these studies have shown improved detection of VT, all of these studies have used existing databases, which have limitations as stated above (i.e., decades old, non-digitized, two-channel ECG and one or two physiologic signals, small sample of patients and arrhythmias, sampling bias, and recordings of short duration).…”

Computerized Decision Support System for Traumatic Brain Injury Management

“…Big data mining is a very active field, 53 particularly pertinent for medical data in the ICU. [54][55][56] Data mining can help uncover links between variables, to help in the development of optimal strategies. Applying data mining techniques to well-structured TBI databases could generate a better understanding of complex brain conditions and aid in selecting the most significant variables to consider in TBI management.…”